Evaluation of sea-ice thickness reanalysis data from the coupled ocean-sea-ice data assimilation system TOPAZ4
Bibliographic record
Abstract
Abstract With the assimilation of satellite-based sea-ice thickness (SIT) data, the new SIT reanalysis from the Towards an Operational Prediction system for the North Atlantic European coastal Zones (TOPAZ4) was released from 2014 to 2018. Apart from assimilating sea-ice concentration and oceanic variables, TOPAZ4 further assimilates CS2SMOS SIT. In this study, the 5-year reanalysis is compared with CS2SMOS, the Pan-Arctic Ice-Ocean Modeling and Assimilating System (PIOMAS) and the Combined Model and Satellite Thickness (CMST). Moreover, we evaluate TOPAZ4 SIT with field observations from upward-looking sonar (ULS), ice mass-balance buoys, Operation IceBridge Quicklook and Sea State Ship-borne Observations. The results indicate TOPAZ4 well reproduces the spatial characteristics of the Arctic SIT distributions, with large differences with CS2SMOS/PIOMAS/CMST mainly restricted to the Atlantic Sector and to the month of September. TOPAZ4 shows thinner ice in March and April, especially to the north of the Canadian Arctic Archipelago with a mean bias of −0.30 m when compared to IceBridge. Besides, TOPAZ4 simulates thicker ice in the Beaufort Sea when compared to ULS, with a mean bias of 0.11 m all year round. The benefit from assimilating SIT data in TOPAZ4 is reflected in a 34% improvement in root mean square deviation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".